1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
|
Matrix :
Predict L1 L2 L3
Actual
L1 3 0 2
L2 0 1 1
L3 0 2 3
Normalized Matrix :
Predict L1 L2 L3
Actual
L1 0.6 0.0 0.4
L2 0.0 0.5 0.5
L3 0.0 0.4 0.6
Overall Statistics :
ACC Macro 0.72222
F1 Macro 0.56515
FPR Macro 0.20952
Kappa 0.35484
NPV Macro 0.77778
Overall ACC 0.58333
PPV Macro 0.61111
SOA1(Landis & Koch) Fair
TPR Macro 0.56667
Zero-one Loss 5
Class Statistics :
Classes L1 L2 L3
ACC(Accuracy) 0.83333 0.75 0.58333
AUC(Area under the ROC curve) 0.8 0.65 0.58571
AUCI(AUC value interpretation) Very Good Fair Poor
F1(F1 score - harmonic mean of precision and sensitivity) 0.75 0.4 0.54545
FN(False negative/miss/type 2 error) 2 1 2
FP(False positive/type 1 error/false alarm) 0 2 3
FPR(Fall-out or false positive rate) 0.0 0.2 0.42857
N(Condition negative) 7 10 7
P(Condition positive or support) 5 2 5
POP(Population) 12 12 12
PPV(Precision or positive predictive value) 1.0 0.33333 0.5
TN(True negative/correct rejection) 7 8 4
TON(Test outcome negative) 9 9 6
TOP(Test outcome positive) 3 3 6
TP(True positive/hit) 3 1 3
TPR(Sensitivity, recall, hit rate, or true positive rate) 0.6 0.5 0.6
One-Vs-All :
L1-Vs-All :
Predict L1 ~
Actual
L1 3 2
~ 0 7
L2-Vs-All :
Predict L2 ~
Actual
L2 1 1
~ 2 8
L3-Vs-All :
Predict L3 ~
Actual
L3 3 2
~ 3 4
|
